Simultaneous Adsorption of Gaseous Hg0 and Hg(II) by Regenerable Monolithic FeMoSx/TiO2: Mechanism and its Application in the Centralized Control of Hg Pollution in Coal-Fired Flue Gas.

FeMoSx/TiO2 was investigated as a regenerable sorbent to simultaneously adsorb Hg0 and Hg(II) from coal-fired flue gas for the centralized control of Hg pollution discharged from coal-fired power plants. The performance of FeMoSx/TiO2 for Hg(II) and/or Hg0 adsorption was evaluated on a fixed-bed reactor at 80 oC, and the mutual interference between Hg0 adsorption and Hg(II) adsorption was analyzed using individual adsorption, simultaneous adsorption, and two-stage adsorption. FeMoSx/TiO2 displayed an excellent capacity for individual Hg0 adsorption (41.8 mg g-1) and a moderate capacity for individual Hg(II) adsorption (0.48 mg g-1). Two types of adsorption sites were present on FeMoSx/TiO2 for gaseous Hg adsorption (S0 and FeS2/MoS3 sites). X-ray photoelectron spectroscope and kinetic analyses demonstrated that Hg0 and Hg(II) could adsorb onto S0 sites, whereas only Hg0 was adsorbed onto FeS2/MoS3 sites. As Hg0 competed with Hg(II) for the S0 sites, the amount of Hg(II) adsorbed slightly decreased by 16% in the presence of Hg0. However, Hg0 adsorption onto the FeS2/MoS3 sites predominated over the Hg0 adsorption onto FeMoSx/TiO2 and it was not inhibited in the presence of Hg(II). Therefore, the amount of Hg0 adsorbed on FeMoSx/TiO2 was only decreased by 2% in the presence of Hg(II).

Distributed Learning Fractal Algorithm for Optimizing a Centralized Control Topology of Wireless Sensor Network Based on the Hilbert Curve L-System.

Wireless sensor networks (WSNs) consist of a large number of small devices or nodes, called micro controller units (MCUs) and located in homes and/or offices, to be operated through the internet from anywhere, making these devices smarter and more efficient. Quality of service routing is one of the critical challenges in WSNs, especially in surveillance systems. To improve the efficiency of the network, in this article we proposes a distributed learning fractal algorithm (DFLA) to design the control topology of a wireless sensor network (WSN), whose nodes are the MCUs distributed in a physical space and which are connected to share parameters of the sensors such as concentrations of C O 2 , humidity, temperature within the space or adjustment of the intensity of light inside and outside the home or office. For this, we start defining the production rules of the L-systems to generate the Hilbert fractal, since these rules facilitate the generation of this fractal, which is a fill-space curve. Then, we model the optimization of a centralized control topology of WSNs and proposed a DFLA to find the best two nodes where a device can find the highly reliable link between these nodes. Thus, we propose a software defined network (SDN) with strong mobility since it can be reconfigured depending on the amount of nodes, also we employ a target coverage because distributed learning fractal algorithm (DLFA) only consider reliable links among devices. Finally, through laboratory tests and computer simulations, we demonstrate the effectiveness of our approach by means of a fractal routing in WSNs, by using a large amount of WSNs devices (from 16 to 64 sensors) for real time monitoring of different parameters, in order to make efficient WSNs and its application in a forthcoming Smart City.

[Plug-in Based Centralized Control System in Operating Rooms].

Centralized equipment controls in an operating room (OR) is crucial to an efficient workflow in the OR. To achieve centralized control, an integrative OR needs to focus on designing a control panel that can appropriately incorporate equipment from different manufactures with various connecting ports and controls. Here we propose to achieve equipment integration using plug-in modules. Each OR will be equipped with a dynamic plug-in control panel containing physically removable connecting ports. Matching outlets will be installed onto the control panels of each equipment used at any given time. This dynamic control panel will be backed with a database containing plug-in modules that can connect any two types of connecting ports common among medical equipment manufacturers. The correct connecting ports will be called using reflection dynamics. This database will be updated regularly to include new connecting ports on the market, making it easy to maintain, update, expand and remain relevant as new equipment are developed. Together, the physical panel and the database will achieve centralized equipment controls in the OR that can be easily adapted to any equipment in the OR.

Different roles of FeS and FeS2 on magnetic FeSx for the selective adsorption of Hg2+ from waste acids in smelters: Reaction mechanism, kinetics, and structure-activity relationship.

Magnetic FeSx was developed as a high-performance sorbent for selectively adsorbing Hg2+ from waste acids in smelters. However, further improvement of its ability for Hg2+ adsorption was extremely restricted due to the lack of reaction mechanisms and structure-activity relationships. In this study, the roles of FeS and FeS2 on magnetic FeSx for Hg2+ adsorption were investigated with alternate adsorption of Hg2+ without/with Cl-. The structure-activity relationship of magnetic FeSx for Hg2+ adsorption and the negative effect of acid erosion were elucidated using kinetic analysis. FeS can react with Hg2+ with 1:1 stoichiometric ratio to form HgS, while FeS2 can react with Hg2+ in the presence of Cl- with novel 1:3 stoichiometric ratio to form Hg3S2Cl2. The rate of magnetic FeSx for Hg2+ adsorption was related to the instantaneous amounts of FeS and threefold FeS2 on magnetic FeSx and the amount of Hg2+ adsorbed. Meanwhile, its capacity for Hg2+ adsorption was related to the initial sum of FeS amount and threefold FeS2 amount on the surface and their ratios by acid erosion. Then, magnetic FeSx-400 was devised with adsorption rate of 2.12 mg g-1 min-1 and capacity of 1092 mg g-1 to recover Hg2+ from waste acids for centralized control.

Design and control of a multi-mobile-robot cooperative transport system based on a novel six degree-of-freedom connector.

Multi-robot cooperative object transport on uneven roads is challenging. The key barrier is dealing with nonholonomic and rigid-formation motion constraints. In this study, to alleviate the influence of these constraints on a multi-robot cooperative transport system (MRCTS), a six degree-of-freedom connector capable of sensing three-axial displacements, three-axial forces, and three-axial angular displacements is designed and employed. Based on the local displacements derived from each connector, we develop a position calibration method to calculate the relative position of each robot and achieve a centralized control strategy. Based on the forces sensed by each connector, we design a decentralized control strategy to accomplish cooperative transport in which a leader robot guides the follower robots toward a destination by applying forces, instead of centralized information broadcasting. The experimental results show that the MRCTS works well on an uneven surface, and the tracking errors are within the design stroke of the connectors, demonstrating the effectiveness of the design and control methods of the MRCTS.

Learning to Centralize Dual-Arm Assembly.

Robotic manipulators are widely used in modern manufacturing processes. However, their deployment in unstructured environments remains an open problem. To deal with the variety, complexity, and uncertainty of real-world manipulation tasks, it is essential to develop a flexible framework with reduced assumptions on the environment characteristics. In recent years, reinforcement learning (RL) has shown great results for single-arm robotic manipulation. However, research focusing on dual-arm manipulation is still rare. From a classical control perspective, solving such tasks often involves complex modeling of interactions between two manipulators and the objects encountered in the tasks, as well as the two robots coupling at a control level. Instead, in this work, we explore the applicability of model-free RL to dual-arm assembly. As we aim to contribute toward an approach that is not limited to dual-arm assembly but dual-arm manipulation in general, we keep modeling efforts at a minimum. Hence, to avoid modeling the interaction between the two robots and the used assembly tools, we present a modular approach with two decentralized single-arm controllers, which are coupled using a single centralized learned policy. We reduce modeling effort to a minimum by using sparse rewards only. Our architecture enables successful assembly and simple transfer from simulation to the real world. We demonstrate the effectiveness of the framework on dual-arm peg-in-hole and analyze sample efficiency and success rates for different action spaces. Moreover, we compare results on different clearances and showcase disturbance recovery and robustness when dealing with position uncertainties. Finally, we zero-shot transfer policies trained in simulation to the real world and evaluate their performance. Videos of the experiments are available at the project website (https://sites.google.com/view/dual-arm-assembly/home).

Centralized PI controller design method for MIMO processes based on frequency response approximation.

A simple method of controller design for multi-input multi-output (MIMO) processes have been proposed in frequency domain. The intensive calculation for the inverse of the process transfer function matrix is simplified to a great extent by evaluating the process transfer function matrix at a low frequency point. The desired closed loop transfer functions are derived for the process using a single tuning parameter for each diagonal element of the process transfer function matrix which represents the desired closed loop time constant. Centralized PI controllers are then designed using a model matching technique by evaluating the transfer functions at a low frequency point. The PI controllers provide acceptable performances for lag dominated as well as time-delay dominated processes and is also applicable to high-dimensional processes. The proposed method is extended for the non-square MIMO processes using two approaches one of which squares up the process transfer function matrix to apply the proposed technique while the other is based on pseudo-inverse evaluation of the process transfer function matrix at a low frequency point.

COVID-19: through the eyes through the front line, an international perspective.

COVID-19 is continuing to ravage the globe. In many Western Countries, the populous has not embraced public health advice which has resulted in a resurgence of the COVID-19 virus. In the United States, there is an absence of a coordinated Federal response. Instead, frontline workers and average citizens are having to cope with extensive mixed messaging regarding mask usage and social distancing from the highest levels of government. This has resulted in the United States not being able to achieve a low level of infection since the pandemic began. In addition, many citizens hold a profound belief that individual freedoms must be preserved, even at the expense of public health; and view the wearing of masks as renouncing this right. These engrained political beliefs can be traced back to the late 1800s. The response of the United States has also been hampered by a highly cost-efficient healthcare system, which does not provide universal care and has a just-in-time supply chain, with far too few supplies in reserve. This efficiency prevented a rapid scaling up of the healthcare response, which resulted in severe deficiencies in available personal protective equipment (PPE) and healthcare staff. To compound issues many healthcare staff are not provided an economic or healthcare safety net. Other frontline workers, such as those who work in transportation and food services, are working under even greater adversities. Many of these workers are from diverse backgrounds, who, along with their families, are at even greater risk for COVID-19. This vulnerable population of frontline workers are faced with a choice of going to work with inadequate PPE or placing food on their families' table. In the United States, official recommendations seem to be ever changing, based more upon supply and test availability, than on science. We must rely on science and learn from the lessons of past pandemics or we will relive, even to a greater degree, the deaths and devastations experienced by our ancestors over 100 years ago.

Centralized PI control for high dimensional multivariable systems based on equivalent transfer function.

This article presents a new scheme to design full matrix controller for high dimensional multivariable processes based on equivalent transfer function (ETF). Differing from existing ETF method, the proposed ETF is derived directly by exploiting the relationship between the equivalent closed-loop transfer function and the inverse of open-loop transfer function. Based on the obtained ETF, the full matrix controller is designed utilizing the existing PI tuning rules. The new proposed ETF model can more accurately represent the original processes. Furthermore, the full matrix centralized controller design method proposed in this paper is applicable to high dimensional multivariable systems with satisfactory performance. Comparison with other multivariable controllers shows that the designed ETF based controller is superior with respect to design-complexity and obtained performance.